Can AI make banks richer? | Credit Risk Prediction using Random Forest

Welcome to Day 2/30 of 30 Days of Machine Learning. In today's problem statement, we build a Credit Risk Prediction model using Random Forest on the German Credit dataset. The video includes a detailed walkthrough of the entire ML workflow, including; 1. Problem Statement and Intuition. 2. Model Selection and Reasoning. 3. Exploratory Data Analysis (EDA). 4. Data Preprocessing Pipeline. 5. Feature Encoding. 6. Mathematical Intuition and Working of Random Forest. 7. Baseline Model and Hyperparameter Tuning using GridSearchCV. 8. Model Evaluation and Performance Metrics. 9. Feature Importance Analysis. 10. Engineering Tradeoffs and Complexity Analysis. This problem statement is part of a 30 day series to get hands on with a variety of Machine Learning problems in a systematic manner. The main focus is to build a strong mathematical intuition behind different Machine Learning models and understand the trade-offs involved in selecting the right model for a particular problem. GitHub Repository : 🔗 https://github.com/Lemniscate-2525/30... Learn More : 🔗 Article 1 :   / introduction-random-forest-classification-...   🔗 Article 2 : https://scikit-learn.org/stable/modul... Dataset : https://archive.ics.uci.edu/ml/datase...) If you enjoyed this video, watch the other videos in the series as well. I'll be uploading a new Machine Learning problem statement every day for the next 30 days. 00:00 Introduction 00:52 Problem Statement and Dataset 03:05 Model Selection and Intuition 05:00 Exploratory Data Analysis and Data Preprocessing 08:09 Imports, EDA and Data Preprocessing Code 19:40 Model Training and Hyperparameter Tuning 26:01 Model Evaluation, Metrics and Feature Importance 30:00 Time Complexity and Space Complexity 32:01 Conclusion and Outro #MachineLearning #Python #DataScience #RandomForest #CreditRiskPrediction #ArtificialIntelligence #ScikitLearn #MLProjects #30DaysOfML #aiml